Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
710756 | IFAC Proceedings Volumes | 2009 | 6 Pages |
Abstract
AbstractThis article presents an algorithm for identification of nonlinear state-space models when the “true” model structure of a process is unknown. In order to estimate the parameters in a state-space model, one needs to know the model structure and have an estimate of states. An approximation of the model structure is obtained using radial basis functions centered around a maximum a posteriori estimate of the state trajectory. A particle filter approximation of smoothed states is then used in conjunction with expectation maximization algorithm for estimating the parameters. The proposed approach is illustrated through an example.
Keywords
Related Topics
Physical Sciences and Engineering
Engineering
Computational Mechanics
Authors
R.Bhushan Gopaluni,